https://jfmp.sbu.ac.ir/ Financial Management Perspective en daily 1 Tue, 21 Apr 2026 00:00:00 +0330 Tue, 21 Apr 2026 00:00:00 +0330 https://jfmp.sbu.ac.ir/article_106955.html Introduction: This study aims to develop and evaluate a novel portfolio optimization framework for enhanced index tracking. Enhanced index tracking is an intermediate strategy between active and passive portfolio management, in which the goal is to construct a portfolio from the constituents of a benchmark index so as to closely follow the index while achieving returns above the benchmark. The main objective is to jointly pursue “return enhancement” and “strict control of tail risk” in financial markets where returns may exhibit skewness, excess kurtosis, and extreme events. In such environments, relying solely on conventional variance-based risk measures may underestimate downside risks and thereby expose portfolio-weighting decisions to substantial losses. The proposed framework employs the Omega ratio as a distribution-based performance measure and Conditional Value at Risk (CVaR) as a downside risk control metric, with the aim of generating excess returns over the benchmark while enhancing the portfolio’s resilience to severe losses.Methods: The proposed framework is formulated as an Omega–CVaR optimization problem. The objective function maximizes the Omega ratio of the tracking portfolio in order to improve the ratio of returns above a specified threshold to losses below that threshold. Simultaneously, CVaR is imposed as a constraint to control downside risk by limiting the mean of large losses in the tail of the return distribution. Operational constraints include full investment, minimum/maximum weight bounds of 0% and 50% to prevent excessive concentration, and asset selection restricted to the constituents of the benchmark index. The empirical assessment is conducted using 30 rolling time windows; in each iteration, 52 weeks of in-sample data are used for estimation and 12 weeks of out-of-sample data are used for performance evaluation. The study period spans approximately eight years (from late January 2018 to late December 2025), and the results are compared with those of the Tehran Exchange Price Index (TEPIX) and a competing model based on conventional constraints/objectives.Finding: The results indicate that the Omega–CVaR framework delivers a substantial long-term advantage in terms of cumulative returns relative to both the TEPIX and the competing model. The cumulative return of the proposed portfolio is reported at 2,546%, compared with 1,350% for the TEPIX. Analyzing the time path of performance across rolling windows shows that the primary advantage of the model does not necessarily stem from “persistent weekly outperformance,” but rather from two complementary mechanisms. First, the CVaR constraint, by limiting the average losses beyond the confidence level, reduces the depth of drawdowns during bearish phases. Second, maximizing the Omega ratio leads to a weight allocation that increases the share of desirable returns relative to undesirable losses, enabling the portfolio to better exploit the “compounding effect” during recovery periods following downturns. Nonetheless, statistical tests reveal that at weekly horizons, the model’s outperformance relative to the TEPIX and competing models is not consistently and significantly confirmed. This pattern is consistent with the defensive nature of the portfolio, as evidenced by an average beta of about 0.43, indicating lower sensitivity to market fluctuations than the TEPIX.Conclusions: The findings suggest that the Omega–CVaR framework is an effective tool for enhanced index tracking in volatile markets characterized by extreme risks. Although statistically significant short-term outperformance is not observed, effective control of severe losses and reinforcement of the compounding effect can ultimately lead to higher cumulative returns over the long run. This strategy is particularly recommended for investors seeking lower risk and greater stability in their portfolios, especially in markets with non-normal return distributions. https://jfmp.sbu.ac.ir/article_107047.html Introduction: Behavioral biases of investment fund managers can have significant negative impacts on fund performance, and to reduce these impacts, fund managers can use advanced analytical tools, structured decision-making processes, and training related to financial psychology. Also, investors can choose more efficient and rationally managed funds by being aware of these aspects. These assessments not only help investors in selecting appropriate funds, but also help managers in improving performance and reducing the negative effects of biased behaviors. Identifying behavioral biases of investment fund managers can also help investors in selecting appropriate funds and reducing the risks arising from incorrect decisions. This issue requires more extensive research and the use of new analytical methods, and in this regard, the present study was conducted with the aim of identifying behavioral biases affecting fund performance in investment fund managers. Methods: The present study is of fundamental and exploratory purpose and the data collection stage is of the library-field type with a qualitative method with a content analysis approach. The participants in this study were 20 experts from investment funds who were selected with a purposeful method based on the conditions of expertise. The resources used in the library section included specialized books in the field of finance and behavioral finance, scientific-research articles published in reputable domestic and foreign journals, academic theses and dissertations, official reports and electronic resources and reputable scientific databases. In the field section, the behavioral biases of managers were identified and localized using the Delphi method and the participation of a panel of experts. The content analysis method and the implementation of the Delphi approach were also used to analyze the data. Also, in order to ensure the reliability and stability of coding, multiple coders were used and the level of agreement between them was calculated. Results and discussion: After the initial identification of 80 behavioral trends in the form of 12 main criteria, the collected data were presented to the selected experts of the 80 behavioral trends identified, 54 trends were able to achieve the initial consensus criterion (mean ≥ 3.5). In the second round, the results and average opinions of the first round were given feedback to the experts. They were then asked to evaluate the trends again. At this stage, 68 behavioral trends were able to achieve the consensus criterion and only 12 trends remained outside the consensus range. In the third round, the focus was on final consolidation of consensus and elimination of ambiguous cases. The results showed that at this stage, 72 behavioral biases were agreed upon by experts in the form of 12 final criteria, which include cognitive biases, emotional biases, personality biases, market biases, risk biases, experiential biases, communication biases, time biases, strategic biases, organizational biases, ethical biases and reporting and informational biases. Conclusions: While confirming the prominent role of behavioral factors in the performance of investment funds, the present study highlights the importance of a combined view of managers' decisions; meaning that combining financial engineering tools with behavioral analysis can reduce the gap between actual decisions and optimal decisions. Thus, this study can provide fund managers, investors, regulatory institutions, and financial policymakers with valuable guidance to improve the decision-making process and enhance the performance of investment funds.